Introducing spatial microsimulation with R: a practical

Abstract

This practical teaches the basic theory and practice of `spatial microsimulation' using the popular
free software package R. The term microsimulation means different things in different disciplines,
so it is important to be clear at the outset what we will and will not be covering.
We will be learning how to create spatial microdata, the basis of all spatial microsimulation
models, using iterative proportional fitting (IPF). IPF is an efficient method for allocating in-
dividuals from a non-spatial dataset to geographical zones, analogous to the `Furness method'
in transport modelling, but with more constraints. There are other ways of generating spatial
microdata but, as far as the author is aware,1 this is the most effective and flexible for many
applications. An alternative approach using the open source `Flexible Modelling Framework' program is described in detail, with worked examples, by Harland (2013).
We will not be learning `dynamic spatial microsimulation' (Ballas et al., 2005): once the spatial
microdata have been generated and integerised, it is up to the user how they are used, be it in an agent based model or as a basis for estimates of income distributions at the local level or whatever.
We thus define spatial microsimulation narrowly in this tutorial as the process of generating spatial microdata (more on this below). The term can also be used to describe a wider approach that harnesses individual-level data allocated to zones for investigating phenomena that vary over space and between individuals such as income inequality or energy overconsumption. In both cases, the generation of spatial microdata is the critical element of the modelling process so the skills learned in this tutorial will provide a firm foundation for further work.